In known relational database management systems (RDBMS), table column level statistical histograms provide vital information to a state-of-the-art SQL optimizer that generates a query plan. With the table column level statistical histograms, the SQL optimizer accurately estimates the cardinality of a single column predicate. Whenever columns are added to the predicate, however, the accuracy of the cardinality estimate is deficient because the table column level statistical histograms fail to consider correlation and affinity between different columns. For instance, in a specific group of 1,000,000 people, the estimated number under the age of 10 is 20% of the total number of people and the estimated number taller than 5 feet 11 inches is 20% of the total number of people. According to these two isolated statistics, there would be an estimated 40,000 (i.e., 20%×20%×1,000,000) people in the specific group who are both under the age of 10 and taller than 5 feet 11 inches. According to common sense, however, this estimate of 40,000 is not reasonable, because people under the age of 10 are very unlikely to be taller than 5 feet 11 inches. An erroneous estimate of the number of rows in earlier stages of the evaluation of a query plan may jeopardize the efficiency of the plan due to the fact that the cost is directly affected by the estimate. Furthermore, known multiple column histogram techniques require a human administrator to take a significant amount of time to manually digest data for detecting multiple column affinity. Still further, many correlations between columns are not easily identifiable or identifiable by a human. Thus, there exists a need to overcome at least one of the preceding deficiencies and limitations of the related art.